Algorithm Selection For Collective Operations In A Parallel Computer

ABSTRACT

Algorithm selection for collective operations in a parallel computer that includes a plurality of compute nodes may include: profiling a plurality of algorithms for each of a set of collective operations, including for each collective operation: executing the operation a plurality times with each execution varying one or more of: geometry, message size, data type, and algorithm to effect the collective operation, thereby generating performance metrics for each execution; storing the performance metrics in a performance profile; at load time of a parallel application including a plurality of parallel processes configured in a particular geometry, filtering the performance profile in dependence upon the particular geometry; during run-time of the parallel application, selecting, for at least one collective operation, an algorithm to effect the operation in dependence upon characteristics of the parallel application and the performance profile; and executing the operation using the selected algorithm.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The field of the invention is data processing, or, more specifically,methods, apparatus, and products for algorithm selection for collectiveoperations in a parallel computer.

2. Description of Related Art

The development of the EDVAC computer system of 1948 is often cited asthe beginning of the computer era. Since that time, computer systemshave evolved into extremely complicated devices. Today's computers aremuch more sophisticated than early systems such as the EDVAC. Computersystems typically include a combination of hardware and softwarecomponents, application programs, operating systems, processors, buses,memory, input/output devices, and so on. As advances in semiconductorprocessing and computer architecture push the performance of thecomputer higher and higher, more sophisticated computer software hasevolved to take advantage of the higher performance of the hardware,resulting in computer systems today that are much more powerful thanjust a few years ago.

Parallel computing is an area of computer technology that hasexperienced advances. Parallel computing is the simultaneous executionof the same task (split up and specially adapted) on multiple processorsin order to obtain results faster. Parallel computing is based on thefact that the process of solving a problem usually can be divided intosmaller tasks, which may be carried out simultaneously with somecoordination.

Parallel computers execute parallel algorithms. A parallel algorithm canbe split up to be executed a piece at a time on many differentprocessing devices, and then put back together again at the end to get adata processing result. Some algorithms are easy to divide up intopieces. Splitting up the job of checking all of the numbers from one toa hundred thousand to see which are primes could be done, for example,by assigning a subset of the numbers to each available processor, andthen putting the list of positive results back together. In thisspecification, the multiple processing devices that execute theindividual pieces of a parallel program are referred to as ‘computenodes.’ A parallel computer is composed of compute nodes and otherprocessing nodes as well, including, for example, input/output (‘I/O’)nodes, and service nodes.

Parallel algorithms are valuable because it is faster to perform somekinds of large computing tasks via a parallel algorithm than it is via aserial (non-parallel) algorithm, because of the way modern processorswork. It is far more difficult to construct a computer with a singlefast processor than one with many slow processors with the samethroughput. There are also certain theoretical limits to the potentialspeed of serial processors. On the other hand, every parallel algorithmhas a serial part and so parallel algorithms have a saturation point.After that point adding more processors does not yield any morethroughput but only increases the overhead and cost.

Parallel algorithms are designed also to optimize one more resource thedata communications requirements among the nodes of a parallel computer.There are two ways parallel processors communicate, shared memory ormessage passing. Shared memory processing needs additional locking forthe data and imposes the overhead of additional processor and bus cyclesand also serializes some portion of the algorithm.

Message passing processing uses high-speed data communications networksand message buffers, but this communication adds transfer overhead onthe data communications networks as well as additional memory need formessage buffers and latency in the data communications among nodes.Designs of parallel computers use specially designed data communicationslinks so that the communication overhead will be small but it is theparallel algorithm that decides the volume of the traffic.

Many data communications network architectures are used for messagepassing among nodes in parallel computers. Compute nodes may beorganized in a network as a ‘torus’ or ‘mesh,’ for example. Also,compute nodes may be organized in a network as a tree. A torus networkconnects the nodes in a three-dimensional mesh with wrap around links.Every node is connected to its six neighbors through this torus network,and each node is addressed by its x,y,z coordinate in the mesh. In sucha manner, a torus network lends itself to point to point operations. Ina tree network, the nodes typically are connected into a binary tree:each node has a parent, and two children (although some nodes may onlyhave zero children or one child, depending on the hardwareconfiguration). Although a tree network typically is inefficient inpoint to point communication, a tree network does provide high bandwidthand low latency for certain collective operations, message passingoperations where all compute nodes participate simultaneously, such as,for example, an allgather operation. In computers that use a torus and atree network, the two networks typically are implemented independentlyof one another, with separate routing circuits, separate physical links,and separate message buffers.

In some parallel computers, each compute node may execute one or moretasks—a process of execution for a parallel application. Each tasks mayinclude a number of endpoints. Each endpoint is a data communicationsendpoint that supports communications among many other endpoints andtasks. In this way, endpoints support collective operations in aparallel computer by supporting the underlying message passingresponsibilities carried out during a collective operation. In someparallel computers, each compute node may execute a single tasksincluding a single endpoint. For example, a parallel computer thatoperates with the Message Passing Interface (‘MPI’) described below inmore detail may execute a single rank on each compute node of theparallel computer. In such implementations, the terms task, endpoint,and rank are effectively synonymous.

Each collective operation may be effected by any one of a number ofalgorithms. Because different algorithms may be optimized for differentcharacteristics of a parallel application, such as geometry, messagesize, and the like, selecting a particular algorithm for execution of acollective operation may be difficult.

SUMMARY OF THE INVENTION

Methods, apparatus, and products for algorithm selection for collectiveoperations in a parallel computer are disclosed in this specification.Each compute node may be configured to execute one or more parallelprocesses of a parallel application. In such a system, algorithmselection for collective operations may include: profiling a pluralityof algorithms for each of a set of collective operations, including foreach collective operation in the set: executing the collective operationa plurality times with each execution varying one or more of: geometry,message size, data type, and algorithm to effect the collectiveoperation, thereby generating performance metrics for each execution;storing the performance metrics in a performance profile; at load timeof a parallel application including a plurality of parallel processesconfigured in a particular geometry, filtering the performance profilein dependence upon the particular geometry; during run-time of theparallel application, selecting, for at least one collective operation,an algorithm to effect the collective operation in dependence upon oneor more characteristics of the parallel application and the filteredperformance profile; and executing the collective operation using theselected algorithm.

The foregoing and other objects, features and advantages of theinvention will be apparent from the following more particulardescriptions of exemplary embodiments of the invention as illustrated inthe accompanying drawings wherein like reference numbers generallyrepresent like parts of exemplary embodiments of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an exemplary system for algorithm selection forcollective operations in a parallel computer according to embodiments ofthe present invention.

FIG. 2 sets forth a block diagram of an example compute node useful in aparallel computer capable of algorithm selection for collectiveoperations in the parallel computer according to embodiments of thepresent invention.

FIG. 3A sets forth a block diagram of an example Point-To-Point Adapteruseful in systems for algorithm selection for collective operations in aparallel computer according to embodiments of the present invention.

FIG. 3B sets forth a block diagram of an example Global CombiningNetwork Adapter useful in systems for algorithm selection for collectiveoperations in a parallel computer according to embodiments of thepresent invention.

FIG. 4 sets forth a line drawing illustrating an example datacommunications network optimized for point-to-point operations useful insystems capable of algorithm selection for collective operations in aparallel computer according to embodiments of the present invention.

FIG. 5 sets forth a line drawing illustrating an example globalcombining network useful in systems capable of algorithm selection forcollective operations in a parallel computer according to embodiments ofthe present invention.

FIG. 6 sets forth a flow chart illustrating an example method foralgorithm selection for collective operations in a parallel computeraccording to embodiments of the present invention.

FIG. 7 sets forth a flow chart illustrating another example method foralgorithm selection for collective operations in a parallel computeraccording to embodiments of the present invention.

FIG. 8 sets forth a flow chart illustrating another example method foralgorithm selection for collective operations in a parallel computeraccording to embodiments of the present invention.

FIG. 9 sets forth a flow chart illustrating another example method foralgorithm selection for collective operations in a parallel computeraccording to embodiments of the present invention.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

Exemplary methods, apparatus, and products for algorithm selection forcollective operations in a parallel computer in accordance with thepresent invention are described with reference to the accompanyingdrawings, beginning with FIG. 1. FIG. 1 illustrates an exemplary systemfor algorithm selection for collective operations in a parallel computeraccording to embodiments of the present invention. The system of FIG. 1includes a parallel computer (100), non-volatile memory for the computerin the form of a data storage device (118), an output device for thecomputer in the form of a printer (120), and an input/output device forthe computer in the form of a computer terminal (122).

The parallel computer (100) in the example of FIG. 1 includes aplurality of compute nodes (102). The compute nodes (102) are coupledfor data communications by several independent data communicationsnetworks including a high speed Ethernet network (174), a Joint TestAction Group (‘JTAG’) network (104), a global combining network (106)which is optimized for collective operations using a binary tree networktopology, and a point-to-point network (108), which is optimized forpoint-to-point operations using a torus network topology. The globalcombining network (106) is a data communications network that includesdata communications links connected to the compute nodes (102) so as toorganize the compute nodes (102) as a binary tree. Each datacommunications network is implemented with data communications linksamong the compute nodes (102). The data communications links providedata communications for parallel operations among the compute nodes(102) of the parallel computer (100).

The compute nodes (102) of the parallel computer (100) are organizedinto at least one operational group (132) of compute nodes forcollective parallel operations on the parallel computer (100). Eachoperational group (132) of compute nodes is the set of compute nodesupon which a collective parallel operation executes. Each compute nodein the operational group (132) is assigned a unique rank that identifiesthe particular compute node in the operational group (132). Collectiveoperations are implemented with data communications among the computenodes of an operational group. Collective operations are those functionsthat involve all the compute nodes of an operational group (132). Acollective operation is an operation, a message-passing computer programinstruction that is executed simultaneously, that is, at approximatelythe same time, by all the compute nodes in an operational group (132) ofcompute nodes. Such an operational group (132) may include all thecompute nodes (102) in a parallel computer (100) or a subset all thecompute nodes (102). Collective operations are often built aroundpoint-to-point operations. A collective operation requires that allprocesses on all compute nodes within an operational group (132) callthe same collective operation with matching arguments. A ‘broadcast’ isan example of a collective operation for moving data among compute nodesof an operational group. A ‘reduce’ operation is an example of acollective operation that executes arithmetic or logical functions ondata distributed among the compute nodes of an operational group (132).An operational group (132) may be implemented as, for example, an MPI‘communicator.’

‘MPI’ refers to ‘Message Passing Interface,’ a prior art parallelcommunications library, a module of computer program instructions fordata communications on parallel computers. Examples of prior-artparallel communications libraries that may be improved for use insystems configured according to embodiments of the present inventioninclude MPI and the ‘Parallel Virtual Machine’ (‘PVM’) library. PVM wasdeveloped by the University of Tennessee, The Oak Ridge NationalLaboratory and Emory University. MPI is promulgated by the MPI Forum, anopen group with representatives from many organizations that define andmaintain the MPI standard. MPI at the time of this writing is a de factostandard for communication among compute nodes running a parallelprogram on a distributed memory parallel computer. This specificationsometimes uses MPI terminology for ease of explanation, although the useof MPI as such is not a requirement or limitation of the presentinvention.

Some collective operations have a single originating or receivingprocess running on a particular compute node in an operational group(132). For example, in a ‘broadcast’ collective operation, the processon the compute node that distributes the data to all the other computenodes is an originating process. In a ‘gather’ operation, for example,the process on the compute node that received all the data from theother compute nodes is a receiving process. The compute node on whichsuch an originating or receiving process runs is referred to as alogical root.

Most collective operations are variations or combinations of four basicoperations: broadcast, gather, scatter, and reduce. The interfaces forthese collective operations are defined in the MPI standards promulgatedby the MPI Forum. Algorithms for executing collective operations,however, are not defined in the MPI standards. In a broadcast operation,all processes specify the same root process, whose buffer contents willbe sent. Processes other than the root specify receive buffers. Afterthe operation, all buffers contain the message from the root process.

A scatter operation, like the broadcast operation, is also a one-to-manycollective operation. In a scatter operation, the logical root dividesdata on the root into segments and distributes a different segment toeach compute node in the operational group (132). In scatter operation,all processes typically specify the same receive count. The sendarguments are only significant to the root process, whose bufferactually contains sendcount * N elements of a given datatype, where N isthe number of processes in the given group of compute nodes. The sendbuffer is divided and dispersed to all processes (including the processon the logical root). Each compute node is assigned a sequentialidentifier termed a ‘rank.’ After the operation, the root has sentsendcount data elements to each process in increasing rank order. Rank 0receives the first sendcount data elements from the send buffer. Rank 1receives the second sendcount data elements from the send buffer, and soon.

A gather operation is a many-to-one collective operation that is acomplete reverse of the description of the scatter operation. That is, agather is a many-to-one collective operation in which elements of adatatype are gathered from the ranked compute nodes into a receivebuffer in a root node.

A reduction operation is also a many-to-one collective operation thatincludes an arithmetic or logical function performed on two dataelements. All processes specify the same ‘count’ and the same arithmeticor logical function. After the reduction, all processes have sent countdata elements from compute node send buffers to the root process. In areduction operation, data elements from corresponding send bufferlocations are combined pair-wise by arithmetic or logical operations toyield a single corresponding element in the root process' receivebuffer. Application specific reduction operations can be defined atruntime. Parallel communications libraries may support predefinedoperations. MPI, for example, provides the following predefinedreduction operations:

MPI_MAX maximum MPI_MIN minimum MPI_SUM sum MPI_PROD product MPI_LANDlogical and MPI_BAND bitwise and MPI_LOR logical or MPI_BOR bitwise orMPI_LXOR logical exclusive or MPI_BXOR bitwise exclusive or

In addition to compute nodes, the parallel computer (100) includesinput/output (‘I/O’) nodes (110, 114) coupled to compute nodes (102)through the global combining network (106). The compute nodes (102) inthe parallel computer (100) may be partitioned into processing sets suchthat each compute node in a processing set is connected for datacommunications to the same I/O node. Each processing set, therefore, iscomposed of one I/O node and a subset of compute nodes (102). The ratiobetween the number of compute nodes to the number of I/O nodes in theentire system typically depends on the hardware configuration for theparallel computer (102). For example, in some configurations, eachprocessing set may be composed of eight compute nodes and one I/O node.In some other configurations, each processing set may be composed ofsixty-four compute nodes and one I/O node. Such example are forexplanation only, however, and not for limitation. Each I/O nodeprovides I/O services between compute nodes (102) of its processing setand a set of I/O devices. In the example of FIG. 1, the I/O nodes (110,114) are connected for data communications I/O devices (118, 120, 122)through local area network (‘LAN’) (130) implemented using high-speedEthernet.

The parallel computer (100) of FIG. 1 also includes a service node (116)coupled to the compute nodes through one of the networks (104). Servicenode (116) provides services common to pluralities of compute nodes,administering the configuration of compute nodes, loading programs intothe compute nodes, starting program execution on the compute nodes,retrieving results of program operations on the compute nodes, and soon. Service node (116) runs a service application (124) and communicateswith users (128) through a service application interface (126) that runson computer terminal (122).

The parallel computer (100) of FIG. 1 operates generally for algorithmselection for collective operations in accordance with embodiments ofthe present invention. To select algorithms for collective operationsany one or more of the compute nodes (102) of the parallel computer(100) of FIG. 1 may profile a plurality of algorithms for each of a setof collective operations. Such profiling may include, for eachcollective operation in the set, executing the collective operation aplurality times with each execution varying one or more of: geometry,message size, data type, and algorithm to effect the collectiveoperation, thereby generating performance metrics for each execution.That is, the parallel computer may profile a number of algorithms for aparticular collective operation by executing the collective operationwith a plurality of permutations of algorithms, characteristics of thecompute nodes executing the collective operation, characteristics of themessage size and data type. Each permutation may be selected independence upon a simulated annealing algorithm. A simulated annealingalgorithm is a generic probabilistic metaheuristic for the globaloptimization problem of locating a good approximation to the globaloptimum of a given function in a large search space. A simulatedannealing algorithm may be utilized when a search space is discrete andmay be more efficient than exhaustive enumeration, especially when thegoal is to find an acceptably good solution in a fixed amount of timerather than the best possible solution.

The term ‘geometry’ as it is used here refers to the size (number ofcompute nodes) and shape (the topology) of a group of compute nodes thatexecute the collective operation. The size of each permutation of thegeometry may be a different power of two. The size of each permutationof the message size may likewise be a different power of two.

The term ‘performance metrics’ refers to any data describing performanceof the collective operation execution. Examples of such performancemetrics may include message passing rate, total bandwidth utilizationfor the execution, total time of execution, total power utilization forthe execution, and other data.

As mentioned above, the profiling may be carried out for a number ofcollective operations and each execution of each collective operationgenerates performance metrics. Upon completing the profiling of the setof collective operations, the parallel computer (100) via one or morecompute nodes (102) may then store the performance metrics in aperformance profile. The performance profile may be broadcast to allcompute nodes of the parallel computer, to a subset of compute nodes, orstored in a well known location accessible by all compute nodes.

At load time of a parallel application that includes a plurality ofparallel processes configured in a particular geometry, each process inthe geometry may filter the performance profile in dependence upon theparticular geometry. Such filtering may remove entries in theperformance profile for geometries that do not match or closely matchthe particular geometry of the processes of the parallel application. Itis noted that the performance profile may or may not include an entrywith an exact match of the particular geometry. In such cases, theparallel process may retain, during filtering, entries having a geometry‘closest’ to the particular geometry based on predefined criteria, suchas a geometries having a size within a predefined range, geometrieshaving a shape with a particular number of dimensions, some combinationof those two, or other criteria.

During run-time of the parallel application, each process may select,for at least one collective operation, an algorithm to effect thecollective operation in dependence upon one or more characteristics ofthe parallel application and the filtered performance profile. Thephrase ‘characteristics of the parallel application’ may refer to anycharacteristic which may affect execution of the collective operationincluding, for example, message size, data type, a quality-of-service(QoS) threshold for bandwidth, maximum power utilization, executiontime, and the like. Once selected, each process of the parallelapplication may then execute the collective operation using the selectedalgorithm.

Algorithm selection for collective operations according to embodimentsof the present invention is generally implemented on a parallel computerthat includes a plurality of compute nodes organized for collectiveoperations through at least one data communications network. In fact,such computers may include thousands of such compute nodes. Each computenode is in turn itself a kind of computer composed of one or morecomputer processing cores, its own computer memory, and its owninput/output adapters. For further explanation, therefore, FIG. 2 setsforth a block diagram of an example compute node (102) useful in aparallel computer capable of algorithm selection for collectiveoperations in the parallel computer according to embodiments of thepresent invention. The compute node (102) of FIG. 2 includes a pluralityof processing cores (165) as well as RAM (156). The processing cores(165) of FIG. 2 may be configured on one or more integrated circuitdies. Processing cores (165) are connected to RAM (156) through ahigh-speed memory bus (155) and through a bus adapter (194) and anextension bus (168) to other components of the compute node. Stored inRAM (156) is an application program (226), a module of computer programinstructions that carries out parallel, user-level data processing usingparallel algorithms.

Also stored RAM (156) is a parallel communications library (161), alibrary of computer program instructions that carry out parallelcommunications among compute nodes, including point-to-point operationsas well as collective operations. A library of parallel communicationsroutines may be developed from scratch for use in systems according toembodiments of the present invention, using a traditional programminglanguage such as the C programming language, and using traditionalprogramming methods to write parallel communications routines that sendand receive data among nodes on two independent data communicationsnetworks. Alternatively, existing prior art libraries may be improved tooperate according to embodiments of the present invention. Examples ofprior-art parallel communications libraries include the ‘Message PassingInterface’ (‘MPI’) library and the ‘Parallel Virtual Machine’ (‘PVM’)library.

Also store din RAM (156) is a profiling application (230), a module ofcomputer program instructions configured to carry out algorithmselection for collective operations in the parallel computer inaccordance with embodiments of the present invention. The profilingapplication (230), when executed, may cause the compute node (102) toprofile a plurality of algorithms for each of a set of collectiveoperations (232), including for each collective operation in the set:executing the collective operation a plurality times with each executionvarying one or more of: geometry, message size, data type, and algorithmto effect the collective operation, thereby generating performancemetrics (236) for each execution. The profiling application (230) maythen store the performance metrics (236) in a performance profile (234).Then, at load time of a parallel application (226) including a pluralityof parallel processes (228) configured in a particular geometry, theparallel processes (228) (which may be instantiated on many computenodes) may filter the performance profile (234) in dependence upon theparticular geometry. During run-time of the parallel application (226),each parallel process (228) may select, for at least one collectiveoperation, an algorithm to effect the collective operation in dependenceupon one or more characteristics of the parallel application and thefiltered performance profile (234) and execute the collective operationusing the selected algorithm.

Also stored in RAM (156) is an operating system (162), a module ofcomputer program instructions and routines for an application program'saccess to other resources of the compute node. It is typical for anapplication program and parallel communications library in a computenode of a parallel computer to run a single thread of execution with nouser login and no security issues because the thread is entitled tocomplete access to all resources of the node. The quantity andcomplexity of tasks to be performed by an operating system on a computenode in a parallel computer therefore are smaller and less complex thanthose of an operating system on a serial computer with many threadsrunning simultaneously. In addition, there is no video I/O on thecompute node (102) of FIG. 2, another factor that decreases the demandson the operating system. The operating system (162) may therefore bequite lightweight by comparison with operating systems of generalpurpose computers, a pared down version as it were, or an operatingsystem developed specifically for operations on a particular parallelcomputer. Operating systems that may usefully be improved, simplified,for use in a compute node include UNIX™, Linux™, Windows XP™, AIX™,IBM's i5/OS™, and others as will occur to those of skill in the art.

The example compute node (102) of FIG. 2 includes several communicationsadapters (172, 176, 180, 188) for implementing data communications withother nodes of a parallel computer. Such data communications may becarried out serially through RS-232 connections, through external busessuch as USB, through data communications networks such as IP networks,and in other ways as will occur to those of skill in the art.Communications adapters implement the hardware level of datacommunications through which one computer sends data communications toanother computer, directly or through a network. Examples ofcommunications adapters useful in parallel computers capable ofalgorithm selection for collective operations in accordance withembodiments of the present invention include modems for wiredcommunications, Ethernet (IEEE 802.3) adapters for wired networkcommunications, and 802.11b adapters for wireless networkcommunications.

The data communications adapters in the example of FIG. 2 include aGigabit Ethernet adapter (172) that couples example compute node (102)for data communications to a Gigabit Ethernet (174). Gigabit Ethernet isa network transmission standard, defined in the IEEE 802.3 standard,that provides a data rate of 1 billion bits per second (one gigabit).Gigabit Ethernet is a variant of Ethernet that operates over multimodefiber optic cable, single mode fiber optic cable, or unshielded twistedpair.

The data communications adapters in the example of FIG. 2 include a JTAGSlave circuit (176) that couples example compute node (102) for datacommunications to a JTAG Master circuit (178). JTAG is the usual nameused for the IEEE 1149.1 standard entitled Standard Test Access Port andBoundary-Scan Architecture for test access ports used for testingprinted circuit boards using boundary scan. JTAG is so widely adaptedthat, at this time, boundary scan is more or less synonymous with JTAG.JTAG is used not only for printed circuit boards, but also forconducting boundary scans of integrated circuits, and is also useful asa mechanism for debugging embedded systems, providing a convenientalternative access point into the system. The example compute node ofFIG. 2 may be all three of these: It typically includes one or moreintegrated circuits installed on a printed circuit board and may beimplemented as an embedded system having its own processing core, itsown memory, and its own I/O capability. JTAG boundary scans through JTAGSlave (176) may efficiently configure processing core registers andmemory in compute node (102) for use in dynamically reassigning aconnected node to a block of compute nodes useful in systems capable ofalgorithm selection for collective operations in a parallel computeraccording to embodiments of the present invention.

The data communications adapters in the example of FIG. 2 include aPoint-To-Point Network Adapter (180) that couples example compute node(102) for data communications to a network (108) that is optimal forpoint-to-point message passing operations such as, for example, anetwork configured as a three-dimensional torus or mesh. ThePoint-To-Point Adapter (180) provides data communications in sixdirections on three communications axes, x, y, and z, through sixbidirectional links: +x (181), −x (182), +y (183), −y (184), +z (185),and −z (186).

The data communications adapters in the example of FIG. 2 include aGlobal Combining Network Adapter (188) that couples example compute node(102) for data communications to a global combining network (106) thatis optimal for collective message passing operations such as, forexample, a network configured as a binary tree. The Global CombiningNetwork Adapter (188) provides data communications through threebidirectional links for each global combining network (106) that theGlobal Combining Network Adapter (188) supports. In the example of FIG.2, the Global Combining Network Adapter (188) provides datacommunications through three bidirectional links for global combiningnetwork (106): two to children nodes (190) and one to a parent node(192).

The example compute node (102) includes multiple arithmetic logic units(‘ALUs’). Each processing core (165) includes an ALU (166), and aseparate ALU (170) is dedicated to the exclusive use of the GlobalCombining Network Adapter (188) for use in performing the arithmetic andlogical functions of reduction operations, including an allreduceoperation. Computer program instructions of a reduction routine in aparallel communications library (161) may latch an instruction for anarithmetic or logical function into an instruction register (169). Whenthe arithmetic or logical function of a reduction operation is a ‘sum’or a ‘logical OR,’ for example, the collective operations adapter (188)may execute the arithmetic or logical operation by use of the ALU (166)in the processing core (165) or, typically much faster, by use of thededicated ALU (170) using data provided by the nodes (190, 192) on theglobal combining network (106) and data provided by processing cores(165) on the compute node (102).

Often when performing arithmetic operations in the global combiningnetwork adapter (188), however, the global combining network adapter(188) only serves to combine data received from the children nodes (190)and pass the result up the network (106) to the parent node (192).Similarly, the global combining network adapter (188) may only serve totransmit data received from the parent node (192) and pass the data downthe network (106) to the children nodes (190). That is, none of theprocessing cores (165) on the compute node (102) contribute data thatalters the output of ALU (170), which is then passed up or down theglobal combining network (106). Because the ALU (170) typically does notoutput any data onto the network (106) until the ALU (170) receivesinput from one of the processing cores (165), a processing core (165)may inject the identity element into the dedicated ALU (170) for theparticular arithmetic operation being perform in the ALU (170) in orderto prevent alteration of the output of the ALU (170). Injecting theidentity element into the ALU, however, often consumes numerousprocessing cycles. To further enhance performance in such cases, theexample compute node (102) includes dedicated hardware (171) forinjecting identity elements into the ALU (170) to reduce the amount ofprocessing core resources required to prevent alteration of the ALUoutput. The dedicated hardware (171) injects an identity element thatcorresponds to the particular arithmetic operation performed by the ALU.For example, when the global combining network adapter (188) performs abitwise OR on the data received from the children nodes (190), dedicatedhardware (171) may inject zeros into the ALU (170) to improveperformance throughout the global combining network (106).

For further explanation, FIG. 3A sets forth a block diagram of anexample Point-To-Point Adapter (180) useful in systems capable ofalgorithm selection for collective operations in a parallel computeraccording to embodiments of the present invention. The Point-To-PointAdapter (180) is designed for use in a data communications networkoptimized for point-to-point operations, a network that organizescompute nodes in a three-dimensional torus or mesh. The Point-To-PointAdapter (180) in the example of FIG. 3A provides data communicationalong an x-axis through four unidirectional data communications links,to and from the next node in the −x direction (182) and to and from thenext node in the +x direction (181). The Point-To-Point Adapter (180) ofFIG. 3A also provides data communication along a y-axis through fourunidirectional data communications links, to and from the next node inthe −y direction (184) and to and from the next node in the +y direction(183). The Point-To-Point Adapter (180) of FIG. 3A also provides datacommunication along a z-axis through four unidirectional datacommunications links, to and from the next node in the −z direction(186) and to and from the next node in the +z direction (185).

For further explanation, FIG. 3B sets forth a block diagram of anexample Global Combining Network Adapter (188) useful in systems capableof algorithm selection for collective operations in a parallel computeraccording to embodiments of the present invention. The Global CombiningNetwork Adapter (188) is designed for use in a network optimized forcollective operations, a network that organizes compute nodes of aparallel computer in a binary tree. The Global Combining Network Adapter(188) in the example of FIG. 3B provides data communication to and fromchildren nodes of a global combining network through four unidirectionaldata communications links (190), and also provides data communication toand from a parent node of the global combining network through twounidirectional data communications links (192).

For further explanation, FIG. 4 sets forth a line drawing illustratingan example data communications network (108) optimized forpoint-to-point operations useful in systems capable of algorithmselection for collective operations in a parallel computer according toembodiments of the present invention. In the example of FIG. 4, dotsrepresent compute nodes (102) of a parallel computer, and the dottedlines between the dots represent data communications links (103) betweencompute nodes. The data communications links are implemented withpoint-to-point data communications adapters similar to the oneillustrated for example in FIG. 3A, with data communications links onthree axis, x, y, and z, and to and fro in six directions +x (181), −x(182), +y (183), −y (184), +z (185), and −z (186). The links and computenodes are organized by this data communications network optimized forpoint-to-point operations into a three dimensional mesh (105). The mesh(105) has wrap-around links on each axis that connect the outermostcompute nodes in the mesh (105) on opposite sides of the mesh (105).These wrap-around links form a torus (107). Each compute node in thetorus has a location in the torus that is uniquely specified by a set ofx, y, z coordinates. Readers will note that the wrap-around links in they and z directions have been omitted for clarity, but are configured ina similar manner to the wrap-around link illustrated in the x direction.For clarity of explanation, the data communications network of FIG. 4 isillustrated with only 27 compute nodes, but readers will recognize thata data communications network optimized for point-to-point operationsfor use in algorithm selection for collective operations in a parallelcomputer in accordance with embodiments of the present invention maycontain only a few compute nodes or may contain thousands of computenodes. For ease of explanation, the data communications network of FIG.4 is illustrated with only three dimensions, but readers will recognizethat a data communications network optimized for point-to-pointoperations for use in algorithm selection for collective operations in aparallel computer in accordance with embodiments of the presentinvention may in facet be implemented in two dimensions, fourdimensions, five dimensions, and so on. Several supercomputers now usefive dimensional mesh or torus networks, including, for example, IBM'sBlue Gene Q™.

For further explanation, FIG. 5 sets forth a line drawing illustratingan example global combining network (106) useful in systems capable ofalgorithm selection for collective operations in a parallel computeraccording to embodiments of the present invention. The example datacommunications network of FIG. 5 includes data communications links(103) connected to the compute nodes so as to organize the compute nodesas a tree. In the example of FIG. 5, dots represent compute nodes (102)of a parallel computer, and the dotted lines (103) between the dotsrepresent data communications links between compute nodes. The datacommunications links are implemented with global combining networkadapters similar to the one illustrated for example in FIG. 3B, witheach node typically providing data communications to and from twochildren nodes and data communications to and from a parent node, withsome exceptions. Nodes in the global combining network (106) may becharacterized as a physical root node (202), branch nodes (204), andleaf nodes (206). The physical root (202) has two children but no parentand is so called because the physical root node (202) is the nodephysically configured at the top of the binary tree. The leaf nodes(206) each has a parent, but leaf nodes have no children. The branchnodes (204) each has both a parent and two children. The links andcompute nodes are thereby organized by this data communications networkoptimized for collective operations into a binary tree (106). Forclarity of explanation, the data communications network of FIG. 5 isillustrated with only 31 compute nodes, but readers will recognize thata global combining network (106) optimized for collective operations foruse in algorithm selection for collective operations in a parallelcomputer in accordance with embodiments of the present invention maycontain only a few compute nodes or may contain thousands of computenodes.

In the example of FIG. 5, each node in the tree is assigned a unitidentifier referred to as a ‘rank’ (250). The rank actually identifies atask or process that is executing a parallel operation according toembodiments of the present invention. Using the rank to identify a nodeassumes that only one such task is executing on each node. To the extentthat more than one participating task executes on a single node, therank identifies the task as such rather than the node. A rank uniquelyidentifies a task's location in the tree network for use in bothpoint-to-point and collective operations in the tree network. The ranksin this example are assigned as integers beginning with 0 assigned tothe root tasks or root node (202), 1 assigned to the first node in thesecond layer of the tree, 2 assigned to the second node in the secondlayer of the tree, 3 assigned to the first node in the third layer ofthe tree, 4 assigned to the second node in the third layer of the tree,and so on. For ease of illustration, only the ranks of the first threelayers of the tree are shown here, but all compute nodes in the treenetwork are assigned a unique rank.

For further explanation, FIG. 6 sets forth a flow chart illustrating anexample method algorithm selection for collective operations in aparallel computer according to embodiments of the present invention. Themethod of FIG. 6 may be carried out in a parallel computer that includesa plurality of compute nodes, such as the example parallel computer ofFIG. 1.

The method of FIG. 6 includes profiling (602), by a profilingapplication (230), a plurality of algorithms for each of a set ofcollective operations. In the method of FIG. 6, profiling (602) aplurality of algorithms for a set of collective operations includes, foreach collective operation in the set, executing (604) the collectiveoperation a plurality times with each execution varying one or more of:geometry, message size, data type, and algorithm to effect thecollective operation. Upon each execution (604), the method of FIG. 6includes generating (606) performance metrics for each execution.

The method of FIG. 6 also includes storing (608) the performance metricsin a performance profile. Storing (608) the performance metrics in aperformance profile may be carried out in various ways including, forexample, by storing an entry for each execution of a collectiveoperation in one or more tables. In an embodiment in which theperformance profile is implemented with a plurality of tables, eachtable may include entries of a particular geometry. Further, such tablesmay be associated through one or more primary or secondary keysrepresenting a collective operation type. That is, tables associatedwith one particular collective operation may include an identicalprimary key.

At load time of a parallel application that includes a plurality ofparallel processes configured in a particular geometry, the method ofFIG. 6 includes filtering (610) the performance profile in dependenceupon the particular geometry. Filtering (610) the performance profilemay be carried out in various ways based on the implementation of theperformance profile. In the example embodiment described above in whichthe performance profile is implemented with a plurality of tables, eachtable representing a different geometry and related to one another bycollective operation type, filtering (610) the performance profile mayinclude removing tables not having a matching or nearly matchinggeometry.

During run-time of the parallel application, the method of FIG. 6includes selecting (612), by each parallel process (228) of the parallelapplication, for at least one collective operation, an algorithm toeffect the collective operation in dependence upon one or morecharacteristics of the parallel application and the filtered performanceprofile. Once selected, the method of FIG. 6 includes executing (614)the collective operation using the selected algorithm.

For further explanation, FIG. 7 sets forth a flow chart illustratinganother example method algorithm selection for collective operations ina parallel computer according to embodiments of the present invention.The method of FIG. 7 is similar to the method of FIG. 6 in that themethod of FIG. 7 may also be carried out in a parallel computer such asthe example parallel computer of FIG. 1. The method of FIG. 7 is alsosimilar to the method of FIG. 6 in that the method of FIG. 7 alsoincludes profiling (602) a plurality of algorithms for each of a set ofcollective operations, storing (608) performance metrics in aperformance profile, filtering (610) the performance profile independence upon the particular geometry, selecting (612) an algorithm toeffect at least one collective operation, and executing the collectiveoperation using the selected algorithm.

The method of FIG. 7 differs from the method of FIG. 6, however, in thatthe method of FIG. 7 also includes monitoring (702), during run time,performance metrics of the selected algorithm. Such monitoring (702) maybe carried out in various ways including tracking bandwidth utilization,CPU utilization, time of execution, and so on.

The method of FIG. 7 also includes updating (706) the performanceprofile with the monitored performance metrics. In some embodiments,updating (706) the performance profile with the monitored performancemetrics may include modifying entries in the performance profile toreflect the monitored performance metrics.

In the example of FIG. 7, monitoring (702) the performance metrics mayinclude identifying (704) a failure during execution of the collectiveoperation. Identifying (704) a failure during execution of thecollective operation may include identifying a hardware failure oridentifying a software failure. To that end, updating (706) theperformance profile may also include removing an entry for the selectedalgorithm in the performance profile.

Such a failure may, in some cases, be the result of the selectedalgorithm. In other cases, such as in the case of a hardware failure,the failure may not be the result of the selected algorithm. To thatend, the method of FIG. 7 may also include clearing (710) a faultrelated to the failure and replacing (712) the entry for the selectedalgorithm in the performance profile.

For further explanation, FIG. 8 sets forth a flow chart illustratinganother example method algorithm selection for collective operations ina parallel computer according to embodiments of the present invention.The method of FIG. 8 is similar to the method of FIG. 6 in that themethod of FIG. 8 may also be carried out in a parallel computer such asthe example parallel computer of FIG. 1. The method of FIG. 8 is alsosimilar to the method of FIG. 6 in that the method of FIG. 8 alsoincludes profiling (602) a plurality of algorithms for each of a set ofcollective operations, storing (608) performance metrics in aperformance profile, filtering (610) the performance profile independence upon the particular geometry, selecting (612) an algorithm toeffect at least one collective operation, and executing the collectiveoperation using the selected algorithm.

The method of FIG. 8 differs from the method of FIG. 6, however, in thatin the method of FIG. 8 filtering (610) the performance profile includesidentifying (802), by each parallel process at geometry create time ofthe parallel application, one of a minimum or average number of parallelprocesses per compute node. In some embodiments, each compute node mayexecute a number of parallel processes of the parallel application thatis different than the number of parallel processes executing on anothernode. Also, the geometries captured in profiling algorithms for the setof collective operations may also include specifications of ‘processesper node.’ Because each process of the parallel application may only beaware of the number of processes on the same node upon which the processis executing (and because different nodes may execute a different numberof processes per node), filtering the performance profile based ongeometry may provide different results for processes on different nodes.To that end, the method of FIG. 8 sets forth a means by which eachprocess identifies, possibly through an ALLREDUCE operation, a samenumber of processes per node to utilize when filtering the performanceprofile. Such a number may be an average number of processes per nodeamong all compute nodes or a minimum number of processes per node. Onceidentified (802), the method of FIG. 8 continues by filtering (804) theperformance profile in dependence upon the identified number.

For further explanation, FIG. 9 sets forth a flow chart illustratinganother example method algorithm selection for collective operations ina parallel computer according to embodiments of the present invention.The method of FIG. 9 is similar to the method of FIG. 6 in that themethod of FIG. 9 may also be carried out in a parallel computer such asthe example parallel computer of FIG. 1. The method of FIG. 9 is alsosimilar to the method of FIG. 6 in that the method of FIG. 9 alsoincludes profiling (602) a plurality of algorithms for each of a set ofcollective operations, storing (608) performance metrics in aperformance profile, filtering (610) the performance profile independence upon the particular geometry, selecting (612) an algorithm toeffect at least one collective operation, and executing the collectiveoperation using the selected algorithm.

The method of FIG. 9 differs from the method of FIG. 6, however, in thatin the method of FIG. 9 profiling (602) a plurality of algorithms foreach of a set of collective operations may include profiling (902) for afirst predetermined period of time, halting (904) the profiling for asecond predetermined period of time, and resuming (906) the profilingafter the conclusion of the second predetermined period of time. In thisway, profiling may be ‘checkpointed’—that is, halted and resumed at alater time—such that power consumption may be managed or actualproduction activities are not impacted by the profiling.

As will be appreciated by one skilled in the art, aspects of the presentinvention may be embodied as a system, method or computer programproduct. Accordingly, aspects of the present invention may take the formof an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present invention may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readabletransmission medium or a computer readable storage medium. A computerreadable storage medium may be, for example, but not limited to, anelectronic, magnetic, optical, electromagnetic, infrared, orsemiconductor system, apparatus, or device, or any suitable combinationof the foregoing. More specific examples (a non-exhaustive list) of thecomputer readable storage medium would include the following: anelectrical connection having one or more wires, a portable computerdiskette, a hard disk, a random access memory (RAM), a read-only memory(ROM), an erasable programmable read-only memory (EPROM or Flashmemory), an optical fiber, a portable compact disc read-only memory(CD-ROM), an optical storage device, a magnetic storage device, or anysuitable combination of the foregoing. In the context of this document,a computer readable storage medium may be any tangible medium that cancontain, or store a program for use by or in connection with aninstruction execution system, apparatus, or device.

A computer readable transmission medium may include a propagated datasignal with computer readable program code embodied therein, forexample, in baseband or as part of a carrier wave. Such a propagatedsignal may take any of a variety of forms, including, but not limitedto, electro-magnetic, optical, or any suitable combination thereof. Acomputer readable transmission medium may be any computer readablemedium that is not a computer readable storage medium and that cancommunicate, propagate, or transport a program for use by or inconnection with an instruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber cable, RF, etc., or any suitable combination ofthe foregoing.

Computer program code for carrying out operations for aspects of thepresent invention may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Smalltalk, C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Aspects of the present invention are described above with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

It will be understood from the foregoing description that modificationsand changes may be made in various embodiments of the present inventionwithout departing from its true spirit. The descriptions in thisspecification are for purposes of illustration only and are not to beconstrued in a limiting sense. The scope of the present invention islimited only by the language of the following claims.

1-8. (canceled)
 9. An apparatus for algorithm selection for collectiveoperations in a parallel computer comprising a plurality of computenodes, each compute node configured to execute one or more parallelprocesses of a parallel application, the apparatus comprising a computerprocessor, a computer memory operatively coupled to the computerprocessor, the computer memory having disposed within it computerprogram instructions that, when executed by the computer processor,cause the apparatus to carry out the steps of: profiling a plurality ofalgorithms for each of a set of collective operations, including foreach collective operation in the set: executing the collective operationa plurality times with each execution varying one or more of: geometry,message size, data type, and algorithm to effect the collectiveoperation, thereby generating performance metrics for each execution;storing the performance metrics in a performance profile; at load timeof a parallel application including a plurality of parallel processesconfigured in a particular geometry, filtering the performance profilein dependence upon the particular geometry; during run-time of theparallel application, selecting, for at least one collective operation,an algorithm to effect the collective operation in dependence upon oneor more characteristics of the parallel application and the filteredperformance profile; and executing the collective operation using theselected algorithm.
 10. The apparatus of claim 9, further comprisingcomputer program instructions that, when executed by the computerprocessor, cause the apparatus to carry out the steps of: monitoring,during run time, performance metrics of the selected algorithm; andupdating the performance profile with the monitored performance metrics.11. The apparatus of claim 10, wherein: monitoring, during run time,performance metrics of the selected algorithm further comprisesidentifying a failure during execution of the collective operation; andupdating the performance profile with the monitored performance metricsfurther comprises removing an entry for the selected algorithm in theperformance profile.
 12. The apparatus of claim 11, further comprisingcomputer program instructions that, when executed by the computerprocessor, cause the apparatus to carry out the steps of: clearing afault related to the failure; and replacing the entry for the selectedalgorithm in the performance profile.
 13. The apparatus of claim 9,wherein profiling the plurality of algorithms for each of the set ofcollective operations further comprises: profiling for a firstpredetermined period of time, halting the profiling for a secondpredetermined period of time, and resuming the profiling after theconclusion of the second predetermined period of time.
 14. The apparatusof claim 9, wherein each varied execution is selected in dependence upona simulated annealing algorithm.
 15. The apparatus of claim 9, whereinfiltering the performance profile in dependence upon the particulargeometry further comprises: identifying, by each parallel process atgeometry create time of the parallel application, one of a minimum oraverage number of parallel processes per compute node; and filtering theperformance profile in dependence upon the identified number.
 16. Theapparatus of claim 9, wherein selecting, for at least one collectiveoperation, an algorithm to effect the collective operation in dependenceupon characteristics of the parallel application and the filteredperformance profile further comprises: identifying entries of thefiltered performance profile for the collective operation; identifying,from the entries for the collective operation, entries including a samenumber of compute nodes as the geometry of the parallel application;identifying, from entries for the collective operation having the samenumber of compute nodes, entries having a same number of parallelprocesses per compute node as the geometry of the parallel application;identifying, from those entries for the collective operation having thesame number of compute nodes and the same number of parallel processes,an entry having a highest performance metric; and selecting thealgorithm related to the entry having the highest performance metric asthe algorithm to effect the collective operation.
 17. A computer programproduct for algorithm selection for collective operations in a parallelcomputer comprising a plurality of compute nodes, each compute nodeconfigured to execute one or more parallel processes of a parallelapplication, the computer program product disposed upon a computerreadable medium, the computer program product comprising computerprogram instructions that, when executed, cause a computer to carry outthe steps of: profiling a plurality of algorithms for each of a set ofcollective operations, including for each collective operation in theset: executing the collective operation a plurality times with eachexecution varying one or more of: geometry, message size, data type, andalgorithm to effect the collective operation, thereby generatingperformance metrics for each execution; storing the performance metricsin a performance profile; at load time of a parallel applicationincluding a plurality of parallel processes configured in a particulargeometry, filtering the performance profile in dependence upon theparticular geometry; during run-time of the parallel application,selecting, for at least one collective operation, an algorithm to effectthe collective operation in dependence upon one or more characteristicsof the parallel application and the filtered performance profile; andexecuting the collective operation using the selected algorithm.
 18. Thecomputer program product of claim 17, further comprising computerprogram instructions that, when executed by the computer processor,cause the apparatus to carry out the steps of: monitoring, during runtime, performance metrics of the selected algorithm; and updating theperformance profile with the monitored performance metrics.
 19. Thecomputer program product of claim 18, wherein: monitoring, during runtime, performance metrics of the selected algorithm further comprisesidentifying a failure during execution of the collective operation; andupdating the performance profile with the monitored performance metricsfurther comprises removing an entry for the selected algorithm in theperformance profile.
 20. The computer program product of claim 19,further comprising computer program instructions that, when executed bythe computer processor, cause the apparatus to carry out the steps of:clearing a fault related to the failure; and replacing the entry for theselected algorithm in the performance profile.
 21. The computer programproduct of claim 17, wherein profiling the plurality of algorithms foreach of the set of collective operations further comprises: profilingfor a first predetermined period of time, halting the profiling for asecond predetermined period of time, and resuming the profiling afterthe conclusion of the second predetermined period of time.
 22. Thecomputer program product of claim 17, wherein each varied execution isselected in dependence upon a simulated annealing algorithm.
 23. Thecomputer program product of claim 17, wherein filtering the performanceprofile in dependence upon the particular geometry further comprises:identifying, by each parallel process at geometry create time of theparallel application, one of a minimum or average number of parallelprocesses per compute node; and filtering the performance profile independence upon the identified number.
 24. The computer program productof claim 17, wherein selecting, for at least one collective operation,an algorithm to effect the collective operation in dependence uponcharacteristics of the parallel application and the filtered performanceprofile further comprises: identifying entries of the filteredperformance profile for the collective operation; identifying, from theentries for the collective operation, entries including a same number ofcompute nodes as the geometry of the parallel application; identifying,from entries for the collective operation having the same number ofcompute nodes, entries having a same number of parallel processes percompute node as the geometry of the parallel application; identifying,from those entries for the collective operation having the same numberof compute nodes and the same number of parallel processes, an entryhaving a highest performance metric; and selecting the algorithm relatedto the entry having the highest performance metric as the algorithm toeffect the collective operation.